Glacier inventories based on visual interpretation and manual delineation of glacier boundaries are time consuming. Supraglacial debris (debris accumulated on glacier terrain) of Himalayan glaciers creates difficulty with automated glacier mapping when using satellite images. In the present study, a combination of band ratio using the TM image and slope parameter was proven to be useful for delineating glaciers’ debris-covered areas. Compared to original TM bands, supervised classification using a combination of principal components two, three, and six of debris and nonglacierized areas facilitated identification of various types of supraglacial debris. Use of principal components four, three, and two of snow- and ice-covered areas as input bands for supervised classification was helpful in classifying different types of snow and ice. Results corresponded well with manually delineated glacier outlines and field observations. Error matrix revealed that the accuracy of classification of the snow- and ice-covered parts of glaciers was 86.29%. Although manual editing was required to differentiate supraglacial debris from periglacial debris (debris outside the glacier boundary), the approach using the ability of morphometric parameter combined with band ratio for delineation of debris-covered parts of glaciers and supervised classification with principal component analysis for mapping of supraglacial covers is observed to be faster than manual delineation.